Data Engineer, Specialist Technology Team (STT), Centralized Data & Analytics

AWS Specialist Technology Team (STT) is the connective tissue between AWS's deep technical specialists, field teams, and customers—delivering L300+ technical expertise, mechanisms, and products that accelerate customer success and drive frictionless AWS adoption at scale. Our mission spans two fronts: we are fundamentally transforming how thousands of field team members access specialist knowledge through AI-powered, on-demand expertise across 30+ technical domains, and we build and ship customer-facing engineered solutions that accelerate AWS service adoption across industries. Our portfolio spans AI-powered specialist knowledge systems (Specialist Agent, Knowledge Vault), hands-on engagement platforms (Workshop Studio), content quality and recommendation engines (Holmes), and go-to-market orchestration tools (Alchemy)—collectively enabling field teams to deliver high-quality technical engagements at scale. These products serve thousands of users across the AWS sales organization, generating rich signals about content effectiveness, engagement delivery, knowledge consumption, and field team productivity. We are seeking a Data Engineer to join our newly formed centralized analytics team as one of the first Data Engineers on the team. This is a greenfield opportunity to build a data platform from the ground up—making foundational architectural decisions and directly influencing how an entire organization measures success and makes investment decisions. You will design, build, and operate scalable data pipelines that connect product telemetry, usage metrics, and business outcomes into a coherent, unified data ecosystem. Your focus will be squarely on engineering—building robust, scalable infrastructure and data models—while dedicated Business Intelligence Engineers on the team own the reporting, dashboarding, and stakeholder-facing analytics. This is not traditional reporting—you will be building the data backbone that powers intelligent, agent-driven analytics experiences (MCP tools, agentic retrieval systems) enabling stakeholders to intuitively access and consume data within their day-to-day workflows. The data you engineer will inform executive reviews, drive product strategy, and power the next generation of self-service analytics tools used by thousands of AWS field team members. Key job responsibilities - Design, build, and operate scalable ETL/ELT pipelines that ingest product telemetry, usage events, and business outcome data from multiple heterogeneous sources across the STT product portfolio - Architect and implement a centralized data platform using AWS-native technologies (Redshift, S3, Glue, Lake Formation, Lambda, Athena) that serves as the single source of truth for organizational analytics - Build and maintain data models that connect product usage signals to business outcomes (e.g., content effectiveness → field engagement → pipeline progression → revenue impact) - Develop data infrastructure supporting AI/ML pipelines and agentic systems, including MCP tools and natural-language data access layers - Implement data quality frameworks with automated monitoring, alerting, and validation to ensure accuracy and reliability as the platform scales - Build self-service data products with clear SLAs, documentation, and governance that reduce ad-hoc request burden and empower stakeholders to answer their own questions - Partner with Applied Scientists and SDE teams to provide clean, well-modeled data for agent evaluation frameworks, retrieval quality measurement, and content effectiveness scoring - Establish data contracts, lineage tracking, and catalog metadata to support discoverability and trust across the organization - Operate with a high bar for operational excellence—owning on-call, monitoring pipeline health, and proactively resolving data freshness or quality issues before they impact consumers - Contribute to the evolution from static dashboards toward agentic data systems by building the